def test_mcu1(self, num_controls):
c = QuantumRegister(num_controls, name='c')
o = QuantumRegister(1, name='o')
allsubsets = list(
chain(*[
combinations(range(num_controls), ni)
for ni in range(num_controls + 1)
]))
for subset in allsubsets:
qc = QuantumCircuit(o, c)
for idx in subset:
qc.x(c[idx])
qc.h(o[0])
qc.mcu1(pi, [c[i] for i in range(num_controls)], o[0])
qc.h(o[0])
for idx in subset:
qc.x(c[idx])
vec = np.asarray(
q_execute(qc, BasicAer.get_backend(
'statevector_simulator')).result().get_statevector(
qc, decimals=16))
vec_o = [0, 1] if len(subset) == num_controls else [1, 0]
# print(vec, np.array(vec_o + [0] * (2 ** (num_controls + num_ancillae + 1) - 2)))
f = state_fidelity(
vec, np.array(vec_o + [0] * (2**(num_controls + 1) - 2)))
self.assertAlmostEqual(f, 1)
def execute(self, circuits):
"""
A wrapper for all algorithms to interface with quantum backend.
Args:
circuits (QuantumCircuit or list[QuantumCircuit]): circuits to execute
Returns:
Result or [Result]: Result objects it will be a list if number of circuits
exceed the maximum number (300)
"""
if not isinstance(circuits, list):
circuits = [circuits]
jobs = []
chunks = int(np.ceil(len(circuits) / self.MAX_CIRCUITS_PER_JOB))
for i in range(chunks):
sub_circuits = circuits[i * self.MAX_CIRCUITS_PER_JOB:(i + 1) *
self.MAX_CIRCUITS_PER_JOB]
jobs.append(
q_execute(sub_circuits, self._backend, **self._execute_config))
if logger.isEnabledFor(logging.DEBUG):
logger.debug(summarize_circuits(circuits))
results = []
for job in jobs:
results.append(job.result(**self._qjob_config))
result = functools.reduce(lambda x, y: x + y, results)
return result
def test_logic_expr_oracle(self, dimacs_str, sols, mct_mode, optimization):
""" Logic Expr oracle test """
num_shots = 1024
leo = LogicalExpressionOracle(dimacs_str,
optimization=optimization,
mct_mode=mct_mode)
leo_circuit = leo.circuit
m = ClassicalRegister(1, name='m')
for assignment in itertools.product([True, False],
repeat=len(leo.variable_register)):
qc = QuantumCircuit(m, leo.variable_register)
for idx, t_f in enumerate(assignment):
if t_f:
qc.x(leo.variable_register[idx])
qc += leo_circuit
qc.barrier(leo.output_register)
qc.measure(leo.output_register, m)
# print(qc.draw(line_length=10000))
counts = q_execute(qc,
BasicAer.get_backend('qasm_simulator'),
shots=num_shots).result().get_counts(qc)
if assignment in sols:
self.assertEqual(counts['1'], num_shots)
else:
self.assertEqual(counts['0'], num_shots)
def test_cnx(self, num_controls, num_ancillae):
c = QuantumRegister(num_controls, name='c')
o = QuantumRegister(1, name='o')
a = QuantumRegister(num_ancillae, name='a')
allsubsets = list(chain(
*[combinations(range(num_controls), ni) for ni in range(num_controls + 1)]))
for subset in allsubsets:
qc = QuantumCircuit(o, c, a)
for idx in subset:
qc.x(c[idx])
qc.cnx(
[c[i] for i in range(num_controls)],
[a[i] for i in range(num_ancillae)],
o[0]
)
for idx in subset:
qc.x(c[idx])
vec = np.asarray(q_execute(qc, qiskit.Aer.get_backend(
'statevector_simulator')).result().get_statevector(qc, decimals=16))
vec_o = [0, 1] if len(subset) == num_controls else [1, 0]
np.testing.assert_almost_equal(
vec,
np.array(vec_o + [0] *
(2 ** (num_controls + num_ancillae + 1) - 2))
)
def construct_circuit(self, mode, register=None):
"""
Construct the statevector of desired initial state.
Args:
mode (string): `vector` or `circuit`. The `vector` mode produces the vector.
While the `circuit` constructs the quantum circuit corresponding that
vector.
register (QuantumRegister): register for circuit construction.
Returns:
QuantumCircuit or numpy.ndarray: statevector.
Raises:
AquaError: when mode is not 'vector' or 'circuit'.
"""
if mode == 'vector':
if self._state_vector is None:
if self._circuit is not None:
self._state_vector = np.asarray(
q_execute(
self._circuit,
BasicAer.get_backend('statevector_simulator')).
result().get_statevector(self._circuit))
return self._state_vector
elif mode == 'circuit':
if self._circuit is None:
if register is None:
register = QuantumRegister(self._num_qubits, name='q')
# create emtpy quantum circuit
circuit = QuantumCircuit()
# if register is actually a list of qubits
if type(register) is list:
# loop over all qubits and add the required registers
for q in register:
if not circuit.has_register(q[0]):
circuit.add_register(q[0])
else:
# if an actual register is given, add it
circuit.add_register(register)
if self._state is None or self._state == 'random':
svc = StateVectorCircuit(self._state_vector)
svc.construct_circuit(circuit, register)
elif self._state == 'zero':
pass
elif self._state == 'uniform':
for i in range(self._num_qubits):
circuit.u2(0.0, np.pi, register[i])
else:
pass
self._circuit = circuit
return self._circuit.copy()
else:
raise AquaError('Mode should be either "vector" or "circuit"')
def construct_circuit(self, mode='circuit', register=None):
# pylint: disable=import-outside-toplevel
from qiskit import BasicAer
if mode == 'vector':
if self._state_vector is None:
if self._circuit is not None:
self._state_vector = np.asarray(
q_execute(
self._circuit,
BasicAer.get_backend('statevector_simulator')).
result().get_statevector(self._circuit))
return self._state_vector
elif mode == 'circuit':
if self._circuit is None:
# create empty quantum circuit
circuit = QuantumCircuit()
if register is None:
register = QuantumRegister(self._num_qubits, name='q')
if isinstance(register, QuantumRegister):
circuit.add_register(register)
elif isinstance(register, list):
for q in register:
if isinstance(q, Qubit):
if not circuit.has_register(q.register):
circuit.add_register(q.register)
else:
raise AquaError('Unexpected qubit type {}.'.format(
type(q)))
else:
raise AquaError('Unexpected register type {}.'.format(
type(register)))
if self._state is None or self._state == 'random':
svc = StateVectorCircuit(self._state_vector)
svc.construct_circuit(circuit=circuit, register=register)
elif self._state == 'uniform':
for i in range(self._num_qubits):
circuit.u(np.pi / 2, 0.0, np.pi, register[i])
elif self._state == 'zero':
pass
else:
AquaError('Unexpected state mode {}.'.format(self._state))
self._circuit = circuit
return self._circuit.copy()
else:
raise AquaError('Mode should be either "vector" or "circuit"')
def test_mct(self, num_controls):
c = QuantumRegister(num_controls, name='c')
o = QuantumRegister(1, name='o')
allsubsets = list(
chain(*[
combinations(range(num_controls), ni)
for ni in range(num_controls + 1)
]))
for subset in allsubsets:
for mode in ['basic', 'advanced', 'noancilla']:
qc = QuantumCircuit(o, c)
if mode == 'basic':
if num_controls <= 2:
num_ancillae = 0
else:
num_ancillae = num_controls - 2
elif mode == 'noancilla':
num_ancillae = 0
else:
if num_controls <= 4:
num_ancillae = 0
else:
num_ancillae = 1
if num_ancillae > 0:
a = QuantumRegister(num_ancillae, name='a')
qc.add_register(a)
for idx in subset:
qc.x(c[idx])
qc.mct([c[i] for i in range(num_controls)],
o[0], [a[i] for i in range(num_ancillae)],
mode=mode)
for idx in subset:
qc.x(c[idx])
vec = np.asarray(
q_execute(qc, get_aer_backend(
'statevector_simulator')).result().get_statevector(
qc, decimals=16))
vec_o = [0, 1] if len(subset) == num_controls else [1, 0]
# print(vec, np.array(vec_o + [0] * (2 ** (num_controls + num_ancillae + 1) - 2)))
f = state_fidelity(
vec,
np.array(vec_o + [0] *
(2**(num_controls + num_ancillae + 1) - 2)))
self.assertAlmostEqual(f, 1)
return
def construct_circuit(self, mode, register=None):
"""
Construct the statevector of desired initial state.
Args:
mode (string): `vector` or `circuit`. The `vector` mode produces the vector.
While the `circuit` constructs the quantum circuit corresponding that
vector.
register (QuantumRegister): register for circuit construction.
Returns:
QuantumCircuit or numpy.ndarray: statevector.
Raises:
ValueError: when mode is not 'vector' or 'circuit'.
"""
if mode == 'vector':
if self._state_vector is None:
if self._circuit is not None:
self._state_vector = np.asarray(
q_execute(self._circuit,
get_aer_backend('statevector_simulator')).
result().get_statevector(self._circuit))
return self._state_vector
elif mode == 'circuit':
if self._circuit is None:
if register is None:
register = QuantumRegister(self._num_qubits, name='q')
circuit = QuantumCircuit(register)
if self._state is None or self._state == 'random':
circuit.initialize(
self._state_vector,
[register[i] for i in range(self._num_qubits)])
circuit = Custom._convert_to_basis_gates(circuit)
elif self._state == 'zero':
pass
elif self._state == 'uniform':
for i in range(self._num_qubits):
circuit.u2(0.0, np.pi, register[i])
else:
pass
self._circuit = circuit
return self._circuit
else:
raise ValueError('Mode should be either "vector" or "circuit"')
def test_sat_oracle(self, cnf_str, sols):
num_shots = 1024
for cnx_mode in ['basic', 'advanced']:
sat = SAT(cnf_str, cnx_mode=cnx_mode)
sat_circuit = sat.construct_circuit()
m = ClassicalRegister(1, name='m')
for assignment in itertools.product([True, False], repeat=len(sat.variable_register())):
qc = QuantumCircuit(m, sat.variable_register())
for idx, tf in enumerate(assignment):
if tf:
qc.x(sat.variable_register()[idx])
qc += sat_circuit
qc.barrier(sat._qr_outcome)
qc.measure(sat._qr_outcome, m)
counts = q_execute(qc, get_aer_backend(
'qasm_simulator'), shots=num_shots).result().get_counts(qc)
if assignment in sols:
assert(counts['1'] == num_shots)
else:
assert(counts['0'] == num_shots)
def test_sat_oracle(self, cnf_str, sols):
num_shots = 1024
sat = get_oracle_instance('SAT')
sat.init_args(cnf_str)
sat_circuit = sat.construct_circuit()
m = ClassicalRegister(1, name='m')
for assignment in itertools.product([True, False],
repeat=len(
sat.variable_register())):
qc = QuantumCircuit(m, sat.variable_register())
for idx, tf in enumerate(assignment):
if tf:
qc.x(sat.variable_register()[idx])
qc += sat_circuit
qc.measure(sat._qr_outcome, m)
counts = q_execute(qc, 'local_qasm_simulator',
shots=num_shots).result().get_counts(qc)
if assignment in sols:
assert (counts['1'] == num_shots)
else:
assert (counts['0'] == num_shots)
def test_mct(self, num_controls, mode):
c = QuantumRegister(num_controls, name='c')
o = QuantumRegister(1, name='o')
subsets = [tuple(range(i)) for i in range(num_controls + 1)]
for subset in subsets:
qc = QuantumCircuit(o, c)
if mode == 'basic':
if num_controls <= 2:
num_ancillae = 0
else:
num_ancillae = num_controls - 2
elif mode == 'noancilla':
num_ancillae = 0
else:
if num_controls <= 4:
num_ancillae = 0
else:
num_ancillae = 1
if num_ancillae > 0:
a = QuantumRegister(num_ancillae, name='a')
qc.add_register(a)
for idx in subset:
qc.x(c[idx])
qc.mct(
[c[i] for i in range(num_controls)],
o[0],
[a[i] for i in range(num_ancillae)],
mode=mode
)
for idx in subset:
qc.x(c[idx])
vec = np.asarray(q_execute(qc, BasicAer.get_backend(
'statevector_simulator')).result().get_statevector(qc, decimals=16))
vec_o = [0, 1] if len(subset) == num_controls else [1, 0]
f = state_fidelity(vec, np.array(vec_o + [0] * (2 ** (num_controls + num_ancillae + 1) - 2)))
self.assertAlmostEqual(f, 1)
def test_mcmt(self, num_controls, num_targets,
single_control_gate_function):
c = QuantumRegister(num_controls, name='c')
o = QuantumRegister(num_targets, name='o')
subsets = [tuple(range(i)) for i in range(num_controls + 1)]
for subset in subsets:
# Expecting some other modes
for mode in ['basic']:
self.log.debug("Subset is {0}".format(subset))
self.log.debug("Num controls = {0}".format(num_controls))
self.log.debug("Num targets = {0}".format(num_targets))
self.log.debug("Gate function is {0}".format(
single_control_gate_function.__name__))
self.log.debug("Mode is {0}".format(mode))
qc = QuantumCircuit(o, c)
# Initialize all targets to 1, just to be sure that
# the generic gate has some effect (f.e. Z gate has no effect
# on a 0 state)
qc.x(o)
if mode == 'basic':
if num_controls <= 1:
num_ancillae = 0
else:
num_ancillae = num_controls - 1
self.log.debug("Num ancillae is {0} ".format(num_ancillae))
if num_ancillae > 0:
a = QuantumRegister(num_ancillae, name='a')
qc.add_register(a)
for idx in subset:
qc.x(c[idx])
qc.mcmt([c[i] for i in range(num_controls)],
[a[i] for i in range(num_ancillae)],
single_control_gate_function,
o,
mode=mode)
for idx in subset:
qc.x(c[idx])
vec = np.asarray(
q_execute(qc, get_aer_backend('statevector_simulator')).
result().get_statevector(qc, decimals=16))
# target register is initially |11...1>, with length equal to 2**(n_targets)
vec_exp = np.array([0] * (2**(num_targets) - 1) + [1])
if (single_control_gate_function.__name__ == "cz"):
# Z gate flips the last qubit only if it's applied an odd
# number of times
if (len(subset) == num_controls
and (num_controls % 2) == 1):
vec_exp[-1] = -1
elif (single_control_gate_function.__name__ == "ch"):
# if all the control qubits have been activated,
# we repeatedly apply the kronecker product of the Hadamard
# with itself and then multiply the results for the original
# state of the target qubits
if (len(subset) == num_controls):
h = 1 / np.sqrt(2) * np.array([[1, 1], [1, -1]])
h_tot = np.array([1])
for i in range(num_targets):
h_tot = np.kron(h_tot, h)
vec_exp = np.dot(h_tot, vec_exp)
else:
raise ValueError("Gate {0} not implementend yet".format(
single_control_gate_function.__name__))
# append the remaining part of the state
vec_exp = np.concatenate(
(vec_exp,
[0] * (2**(num_controls + num_ancillae + num_targets) -
vec_exp.size)))
f = state_fidelity(vec, vec_exp)
self.assertAlmostEqual(f, 1)
def test_evolution(self):
SIZE = 2
# SPARSITY = 0
# X = [[0, 1], [1, 0]]
# Y = [[0, -1j], [1j, 0]]
Z = [[1, 0], [0, -1]]
_I = [[1, 0], [0, 1]]
# + 0.5 * np.kron(Y, X)# + 0.3 * np.kron(Z, X) + 0.4 * np.kron(Z, Y)
h1 = np.kron(_I, Z)
# np.random.seed(2)
temp = np.random.random((2**SIZE, 2**SIZE))
h1 = temp + temp.T
qubit_op = Operator(matrix=h1)
# qubit_op_jw.chop_by_threshold(10 ** -10)
if qubit_op.grouped_paulis is None:
qubit_op._matrix_to_paulis()
qubit_op._paulis_to_grouped_paulis()
for ps in qubit_op.grouped_paulis:
for p1 in ps:
for p2 in ps:
if p1 != p2:
np.testing.assert_almost_equal(
p1[1].to_matrix() @ p2[1].to_matrix(),
p2[1].to_matrix() @ p1[1].to_matrix())
state_in = Custom(SIZE, state='random')
evo_time = 1
num_time_slices = 3
# announces params
self.log.debug('evo time: {}'.format(evo_time))
self.log.debug('num time slices: {}'.format(num_time_slices))
self.log.debug('state_in: {}'.format(state_in._state_vector))
# get the exact state_out from raw matrix multiplication
state_out_exact = qubit_op.evolve(
state_in=state_in.construct_circuit('vector'),
evo_time=evo_time,
evo_mode='matrix',
num_time_slices=0)
# self.log.debug('exact:\n{}'.format(state_out_exact))
qubit_op_temp = copy.deepcopy(qubit_op)
for expansion_mode in ['trotter', 'suzuki']:
self.log.debug('Under {} expansion mode:'.format(expansion_mode))
for expansion_order in [1, 2, 3, 4
] if expansion_mode == 'suzuki' else [1]:
# assure every time the operator from the original one
qubit_op = copy.deepcopy(qubit_op_temp)
if expansion_mode == 'suzuki':
self.log.debug(
'With expansion order {}:'.format(expansion_order))
state_out_matrix = qubit_op.evolve(
state_in=state_in.construct_circuit('vector'),
evo_time=evo_time,
evo_mode='matrix',
num_time_slices=num_time_slices,
expansion_mode=expansion_mode,
expansion_order=expansion_order)
quantum_registers = QuantumRegister(qubit_op.num_qubits,
name='q')
qc = QuantumCircuit(quantum_registers)
qc += state_in.construct_circuit('circuit', quantum_registers)
qc += qubit_op.evolve(
evo_time=evo_time,
evo_mode='circuit',
num_time_slices=num_time_slices,
quantum_registers=quantum_registers,
expansion_mode=expansion_mode,
expansion_order=expansion_order,
)
job = q_execute(qc,
BasicAer.get_backend('statevector_simulator'))
state_out_circuit = np.asarray(job.result().get_statevector(
qc, decimals=16))
self.log.debug(
'The fidelity between exact and matrix: {}'.format(
state_fidelity(state_out_exact, state_out_matrix)))
self.log.debug(
'The fidelity between exact and circuit: {}'.format(
state_fidelity(state_out_exact, state_out_circuit)))
f_mc = state_fidelity(state_out_matrix, state_out_circuit)
self.log.debug(
'The fidelity between matrix and circuit: {}'.format(f_mc))
self.assertAlmostEqual(f_mc, 1)
def test_evolution(self):
SIZE = 2
#SPARSITY = 0
#X = [[0, 1], [1, 0]]
#Y = [[0, -1j], [1j, 0]]
Z = [[1, 0], [0, -1]]
I = [[1, 0], [0, 1]]
# + 0.5 * np.kron(Y, X)# + 0.3 * np.kron(Z, X) + 0.4 * np.kron(Z, Y)
h1 = np.kron(I, Z)
# np.random.seed(2)
temp = np.random.random((2 ** SIZE, 2 ** SIZE))
h1 = temp + temp.T
qubitOp = Operator(matrix=h1)
# qubitOp_jw.chop_by_threshold(10 ** -10)
# self.log.debug('matrix:\n{}\n'.format(qubitOp.matrix))
# self.log.debug('paulis:')
# self.log.debug(qubitOp.print_operators('paulis'))
if qubitOp.grouped_paulis is None:
qubitOp._matrix_to_paulis()
qubitOp._paulis_to_grouped_paulis()
for ps in qubitOp.grouped_paulis:
for p1 in ps:
for p2 in ps:
if p1 != p2:
np.testing.assert_almost_equal(
p1[1].to_matrix() @ p2[1].to_matrix(),
p2[1].to_matrix() @ p1[1].to_matrix()
)
flattened_grouped_paulis = [
pauli for group in qubitOp.grouped_paulis for pauli in group[1:]]
state_in = get_initial_state_instance('CUSTOM')
state_in.init_args(SIZE, state='random')
evo_time = 1
num_time_slices = 1
# announces params
self.log.debug('evo time: {}'.format(evo_time))
self.log.debug('num time slices: {}'.format(num_time_slices))
self.log.debug('state_in: {}'.format(state_in._state_vector))
# get the exact state_out from raw matrix multiplication
state_out_exact = qubitOp.evolve(
state_in.construct_circuit('vector'), evo_time, 'matrix', 0)
# self.log.debug('exact:\n{}'.format(state_out_exact))
qubitOp_temp = copy.deepcopy(qubitOp)
for grouping in ['default', 'random']:
self.log.debug('Under {} paulis grouping:'.format(grouping))
for expansion_mode in ['trotter', 'suzuki']:
self.log.debug(
'Under {} expansion mode:'.format(expansion_mode))
for expansion_order in [1, 2, 3, 4] if expansion_mode == 'suzuki' else [1]:
# assure every time the operator from the original one
qubitOp = copy.deepcopy(qubitOp_temp)
if expansion_mode == 'suzuki':
self.log.debug(
'With expansion order {}:'.format(expansion_order))
state_out_matrix = qubitOp.evolve(
state_in.construct_circuit(
'vector'), evo_time, 'matrix', num_time_slices,
paulis_grouping=grouping,
expansion_mode=expansion_mode,
expansion_order=expansion_order
)
quantum_registers = QuantumRegister(qubitOp.num_qubits)
qc = state_in.construct_circuit(
'circuit', quantum_registers)
qc += qubitOp.evolve(
None, evo_time, 'circuit', num_time_slices,
quantum_registers=quantum_registers,
paulis_grouping=grouping,
expansion_mode=expansion_mode,
expansion_order=expansion_order,
)
job = q_execute(qc, qiskit.Aer.get_backend(
'statevector_simulator'), skip_transpiler=True)
state_out_circuit = np.asarray(
job.result().get_statevector(qc))
self.log.debug('The fidelity between exact and matrix: {}'.format(
state_fidelity(state_out_exact, state_out_matrix)
))
self.log.debug('The fidelity between exact and circuit: {}'.format(
state_fidelity(state_out_exact, state_out_circuit)
))
f_mc = state_fidelity(state_out_matrix, state_out_circuit)
self.log.debug(
'The fidelity between matrix and circuit: {}'.format(f_mc))
self.assertAlmostEqual(f_mc, 1)
def run_exp(num_reps=1):
# choice of Hi basis
H_basis = [Pauli.from_label(p) for p in ['II', 'ZI', 'IZ', 'ZZ', 'YY', 'XX']]
num_qubits = 2
evo_time = 1
epsilon = 0.1
L = 32 ## number of local hamiltonian terms
############################################################
# Generate a random Hamiltonian H as the sum of m basis Hi operators
############################################################
hs = np.random.random(L)
indexes = np.random.randint(low=0, high=6, size=L)
## H in matrix form
H_matrix = np.zeros((2 ** num_qubits, 2 ** num_qubits))
## H as a list of pauli operators (unweighted)
H_list = []
for i in range(L):
H_matrix = H_matrix + hs[i] * H_basis[indexes[i]].to_matrix()
H_list.append(H_basis[indexes[i]])
print('matrix H: \n', H_matrix)
# H as a pauli operator
H_qubitOp = op_converter.to_weighted_pauli_operator(MatrixOperator(matrix=H_matrix))
# Generate an initial state
state_in = Custom(num_qubits, state='random')
############################################################
# Ground truth and benchmarks
############################################################
# Ground truth
state_in_vec = state_in.construct_circuit('vector')
groundtruth = expm(-1.j * H_matrix * evo_time) @ state_in_vec
print('The directly computed groundtruth evolution result state is')
print('{}\n.'.format(groundtruth))
# Build circuit using Qiskit's evolve algorithm, which based on Trotter-Suzuki.
quantum_registers = QuantumRegister(num_qubits)
circuit = state_in.construct_circuit('circuit', quantum_registers)
circuit += H_qubitOp.evolve(
None, evo_time, num_time_slices=10,
quantum_registers=quantum_registers,
expansion_mode='suzuki',
expansion_order=1
)
# Simulate Trotter-Suzuki circuit and print it
backend = BasicAer.get_backend('statevector_simulator')
job = q_execute(circuit, backend)
circuit_execution_result = np.asarray(job.result().get_statevector(circuit))
print('The simulated (suzuki) evolution result state is')
print('{}\n'.format(circuit_execution_result))
# The difference between the ground truth and the simulated state
# measured by "Fidelity"
fidelity_suzuki = state_fidelity(groundtruth, circuit_execution_result)
print('Fidelity between the groundtruth and the circuit result states is {}.'.format(fidelity_suzuki))
print('\n')
############################################################
# Our qdrift implementation
############################################################
quantum_registers = QuantumRegister(num_qubits)
circuit = state_in.construct_circuit('circuit', quantum_registers)
# Contruct the circuit which implements qdrift
circuit = time_evolve_qubits(quantum_registers, circuit, num_qubits, H_list, hs, evo_time, epsilon, num_reps)
# Simulate circuit and print it
backend = BasicAer.get_backend('statevector_simulator')
job = q_execute(circuit, backend)
circuit_execution_result = np.asarray(job.result().get_statevector(circuit))
print('The simulated (qdrift) evolution result state is\n{}.'.format(circuit_execution_result))
print('\n')
# Measure the fidelity
fidelity_qdrift = state_fidelity(groundtruth, circuit_execution_result)
print('Fidelity between the groundtruth and the circuit result states is {}.'.format(fidelity_qdrift))
print('\n')
print('benchmark, suzuki:', fidelity_suzuki)
print('qdrift:', fidelity_qdrift)
return fidelity_qdrift, fidelity_suzuki
